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Traditional taxonomy provides a hierarchical organization of bacte- ria and archaea across taxonomic ranks from kingdom to subspecies. More recently, bacterial taxonomy has been more robustly quanti- fied using comparisons of sequenced genomes, as in the Genome Taxonomy Database (GTDB), resolving down to genera and species. Such taxonomies have proven useful in many contexts, yet lack the flexibility and resolution of a more fine-grained approach. We apply our Life Identification Number (LIN) approach as a com- mon, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based dis- crimination of taxa, and extend taxonomic identification below the species level in a principled way. We utilize our existing concept of a LINgroup as an organizational concept for microorganisms that are closely related by overall genomic similarity, to help resolve some of the confusions and unforeseen negative effects of nomen- clature changes of microbes due to genome-based reclassification. Our results obtained from experimentation demonstrate the value of LINs and LINgroups in mapping between taxonomies, translat- ing between different nomenclatures, and integrating them into a single taxonomic framework. 

Genomics has put prokaryotic rank-based taxonomy on a solid phylogenetic foundation. However, most taxonomic ranks were set long before the advent of DNA sequencing and genomics. In this concept paper, we thus ask the following question: should prokaryotic classification schemes besides the current phylum-to-species ranks be explored, developed, and incorporated into scientific discourse? Could such alternative schemes provide better solutions to the basic need of science and society for which taxonomy was developed, namely, precise and meaningful identification? A neutral genome-similarity based framework is then described that could allow alternative classification schemes to be explored, compared, and translated into each other without having to choose only one as the gold standard. Classification schemes could thus continue to evolve and be selected according to their benefits and based on how well they fulfill the need for prokaryotic identification. 

Abstract As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these practices in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field. 

ABSTRACT Here, we report the genome sequence of Tenacibaculum mesophilum strain ECR, which was isolated from the river/ocean interface at Trunk River in Falmouth, Massachusetts. The isolation and sequencing were performed as part of the 2016 and 2018 Microbial Diversity courses at the Marine Biological Laboratory in Woods Hole, Massachusetts. 

Clues to the evolutionary steps producing innovations in oxygenic photosynthesis may be preserved in the genomes of organisms phylogenetically placed between non-photosynthetic Vampirovibrionia (formerly Melainabacteria) and the thylakoid-containing Cyanobacteria. However, only two species with published genomes are known to occupy this phylogenetic space, both within the genus Gloeobacter. Here, we describe nearly complete, metagenome-assembled genomes (MAGs) of an uncultured organism phylogenetically placed near Gloeobacter, for which we propose the name Candidatus Aurora vandensis {Au’ro.ra. L. fem. n. aurora, the goddess of the dawn in Roman mythology; van.de’nsis. N.L. fem. adj. vandensis of Lake Vanda, Antarctica}. The MAG of A. vandensis contains homologs of most genes necessary for oxygenic photosynthesis including key reaction center proteins. Many accessory subunits associated with the photosystems in other species either are missing from the MAG or are poorly conserved. The MAG also lacks homologs of genes associated with the pigments phycocyanoerethrin, phycoeretherin and several structural parts of the phycobilisome. Additional characterization of this organism is expected to inform models of the evolution of oxygenic photosynthesis. 

The sourmash software package uses MinHash-based sketching to create “signatures”, compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.